Achieving Data Privacy through Secrecy Views and Null-Based Virtual Updates

Bertossi, Leopoldo; Li, Lechen

doi:10.1109/tkde.2012.86

Cited by 21 publications

(37 citation statements)

References 32 publications

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“…The definition of QA-causality applies to monotone queries [47,48]. 5 How- 3 In contrast with general causal claims, such as "smoking causes cancer", which refer some sort of related events, actual causation specifies a particular instantiation of a causal relationship, e.g., "Joe's smoking is a cause for his cancer". 4 As discussed in [59], some objections to the Halpern-Pearl model of causality and the corresponding changes [35,36] do not affect results in the context of databases.…”

Section: Introductionmentioning

confidence: 99%

“…4 As discussed in [59], some objections to the Halpern-Pearl model of causality and the corresponding changes [35,36] do not affect results in the context of databases. 5 That a query is monotone means that the set of answers may only grow when new tuples ever, all complexity and algorithmic results in [47,59] have been restricted to first-order (FO) monotone queries, mainly conjunctive queries. However, Datalog queries [13,1], which are also monotone, but may contain recursion, require investigation in the context of QA-causality.…”

Section: Introductionmentioning

confidence: 99%

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Causes for query answers from databases: Datalog abduction, view-updates, and integrity constraints

Bertossi

Salimi

2017

International Journal of Approximate Reasoning

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Causality has been recently introduced in databases, to model, characterize, and possibly compute causes for query answers. Connections between QAcausality and consistency-based diagnosis and database repairs (wrt. integrity constraint violations) have already been established. In this work we establish precise connections between QA-causality and both abductive diagnosis and the view-update problem in databases, allowing us to obtain new algorithmic and complexity results for QA-causality. We also obtain new results on the complexity of view-conditioned causality, and investigate the notion of QA-causality in the presence of integrity constraints, obtaining complexity results from a connection with view-conditioned causality. The abduction connection under integrity constraints allows us to obtain algorithmic tools for QA-causality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Causes for query answers from databases: Datalog abduction, view-updates, and integrity constraints

Bertossi

Salimi

2017

International Journal of Approximate Reasoning

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“…7 Adapting inc-deg s,g 3 to attribute-based repairs Database repairs that are based on changes of attribute values in tuples have been considered in [54,10], and implicitly in [8]. We rely here on repairs introduced in [6], which we briefly present by means of an example.…”

Section: Inconsistency Degree Under Updatesmentioning

confidence: 99%

“…For certain forms of prioritized repairs, such as endogenous repairs, the normalization coefficient |D| might be unnecessarily large. In this particular case, it might be better to use |D n | 8. This approach was followed in[6] to compute maximum responsibility degrees of database tuples as causes for violations of DCs, appealing to a causality-repair connection[11].…”

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confidence: 99%

Repair-Based Degrees of Database Inconsistency

Bertossi¹

2019

Lecture Notes in Computer Science

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We propose a generic numerical measure of the inconsistency of a database with respect to a set of integrity constraints. It is based on an abstract repair semantics. In particular, an inconsistency measure associated to cardinalityrepairs is investigated in detail. More specifically, it is shown that it can be computed via answer-set programs, but sometimes its computation can be intractable in data complexity. However, polynomial-time deterministic and randomized approximations are exhibited. The behavior of this measure under small updates is analyzed, obtaining fixed-parameter tractability results. Furthermore, alternative inconsistency measures are proposed and discussed.

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“…The causes for the query, represented by their tids, can be obtained by posing simple queries to the program under the uncertain or brave semantics that makes true what is true in some model of the repair-ASP. 4 In this case, Π(D, κ(Q)) |= brave Ans(t), where the auxiliary predicate is defined on top of Π(D, κ(Q)) by the rules: Ans(t) ← R ′ (t, x, y, d) and Ans(t) ← S ′ (t, x, d).…”

Section: Causality Answer Set Programsmentioning

confidence: 99%

The Causality/Repair Connection in Databases: Causality-Programs

Bertossi

2017

Lecture Notes in Computer Science

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In this work, answer-set programs that specify repairs of databases are used as a basis for solving computational and reasoning problems about causes for query answers from databases. IntroductionCausality appears at the foundations of many scientific disciplines. In data and knowledge management, the need to represent and compute causes may be related to some form of uncertainty about the information at hand. More specifically in data management, we need to understand why certain results, e.g. query answers, are obtained or not. Or why certain natural semantic conditions are not satisfied. These tasks become more prominent and difficult when dealing with large volumes of data. One would expect the database to provide explanations, to understand, explore and make sense of the data, or to reconsider queries and integrity constraints (ICs). Causes for data phenomena can be seen as a kind of explanations.Seminal work on causality in DBs introduced in [17], and building on work on causality as found in artificial intelligence, appeals to the notions of counterfactuals, interventions and structural models [15]. Actually, [17] introduces the notions of: (a) a DB tuple as an actual cause for a query result, (b) a contingency set for a cause, as a set of tuples that must accompany the cause for it to be such, and (c) the responsibility of a cause as a numerical measure of its strength (building on [11]).Most of our research on causality in DBs has been motivated by an attempt to understand causality from different angles of data and knowledge management. In [6], precise reductions between causality in DBs, DB repairs, and consistency-based diagnosis were established; and the relationships where investigated and exploited. In [7], causality in DBs was related to view-based DB updates and abductive diagnosis. These are all interesting and fruitful connections among several forms of non-monotonic reasoning; each of them reflecting some form of uncertainty about the information at hand. In the case of DB repairs [3], it is about the uncertainty due the non-satisfaction of given ICs, which is represented by presence of possibly multiple intended repairs of the inconsistent DB.DB repairs can be specified by means of answer-set programs (or disjunctive logic programs with stable model semantics) [14], the so-called repair-programs. Cf. [10,3] for repair-programs and additional references. In this work we exploit the reduction of DB causality to DB repairs established in [6], by taking advantage of repair programs for specifying and computing causes, their contingency sets, and their responsibility degrees. We show that that the resulting causality-programs have the necessary and sufficient expressive power to capture and compute not only causes, which can be done with ⋆

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Achieving Data Privacy through Secrecy Views and Null-Based Virtual Updates

Cited by 21 publications

References 32 publications

Causes for query answers from databases: Datalog abduction, view-updates, and integrity constraints

Causes for query answers from databases: Datalog abduction, view-updates, and integrity constraints

Repair-Based Degrees of Database Inconsistency

The Causality/Repair Connection in Databases: Causality-Programs

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